Frequent Cannabis Use and Cessation of Injection of Opioids, Vancouver, Canada, 2005–2018
Bibliographic record
Abstract
Objectives. To evaluate the impact of frequent cannabis use on injection cessation and injection relapse among people who inject drugs (PWID). Methods. Three prospective cohorts of PWID from Vancouver, Canada, provided the data for these analyses. We used extended Cox regression analysis with time-updated covariates to analyze the association between cannabis use and injection cessation and injection relapse. Results. Between 2005 and 2018, at-least-daily cannabis use was associated with swifter rates of injection cessation (adjusted hazard ratio [AHR] = 1.16; 95% confidence interval [CI] = 1.03, 1.30). A subanalysis revealed that this association was only significant for opioid injection cessation (AHR = 1.26; 95% CI = 1.12, 1.41). At-least-daily cannabis use was not significantly associated with injection relapse (AHR = 1.08; 95% CI = 0.95, 1.23). Conclusions. We observed that at-least-daily cannabis use was associated with a 16% increase in the hazard rate of injection cessation, and this effect was restricted to the cessation of injection opioids. This finding is encouraging given the uncertainty surrounding the impact of cannabis policies on PWID during the ongoing opioid overdose crisis in many settings in the United States and Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".